The precision–fragility paradox: How generative AI raises customer lifetime value but increases stockout risks in retail

Abstract

Retailers employing generative AI for hyper-personalization experience a notable increase of 37% in customer lifetime value. They incur hidden operational costs, with a 29% increase in stockouts during disruptions. The Precision-Fragility Paradox emerges when surgical customer segmentation partitions demand streams, thereby constraining supply chain agility and heightening systemic vulnerability. This study examines the tension by proposing an integrated theoretical framework that demonstrates how adaptive service modularity aligns hyper-personalization with operational resilience. The research utilizes a robust methodological triangulation, integrating agent-based modeling of 50 million transactions with a longitudinal field experiment involving multinational retailers. It delineates an existential threshold at which personalization exceeds 18.3% Demand Sensing. Heightened granularity amplifies the risk of fragility by a factor of 2.4, leading to a non-linear increase in stockouts. The research indicates that this fragility is not inevitable: organizations implementing modular architectures improve their reconfiguration capacity by 41% while preserving 92% of revenue gains. AI-driven resilience mechanisms reduce recovery latency by 63% through autonomous supplier rerouting and dynamic inventory adaptation. The findings indicate a service-dynamic capability architecture where real-time personalization governance, liquefiable resource networks, and self-calibrating systems transform volatility from a threat into an advantage. This blueprint allows executives to balance precision and flexibility, utilizing AI's revenue potential while reducing operational externalities. The study redefines competitive resilience, demonstrating that in algorithm-driven commerce, true robustness is derived not from enduring shocks, but from creating systems that adapt amidst disruption.